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A novel adaptive detection framework that combines blockchain-based integrity verification with explainable artificial intelligence to identify and mitigate poisoning attempts in federated learning systems for electric vehicle charging networks is presented.
The imminent threat posed by quantum computing to classical cryptographic systems necessitates the development of quantum-resistant, efficient, and scalable encryption techniques, especially for real-time distributed optimization networks critical to national infrastructure. This study introduces a novel lightweight post-quantum cryptographic algorithm tailored for secure real-time decision-making in decentralized systems such as smart grids, autonomous transport, and defense communication networks. This study proposes a lattice-based encryption scheme optimized for low-latency and bandwidth-constrained environments, integrating a parameterized Learning With Errors (LWE) framework with a compressed key encapsulation mechanism (KEM). The cryptographic algorithm is coupled with an adaptive distributed optimization protocol that dynamically adjusts computation and communication loads across agents to maintain performance under cryptographic overhead. The method is rigorously analyzed in terms of computational complexity, security assumptions, and operational feasibility. Simulation experiments are conducted over dynamic networks modeled on real-world distributed control systems, comparing performance metrics such as encryption latency, decision throughput, and fault tolerance against state-of-the-art schemes. Results demonstrate significant improvements in end-to-end delay, with cryptographic integrity maintained under adversarial conditions, establishing the scheme’s applicability for post-quantum real-time systems. The contributions of this work bridge the critical research gap between post-quantum cryptography and real-time optimization, reinforcing secure decision-making in systems of national interest.
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